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raykkk
325
deep_research/agent_deep_research/utils.py
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325
deep_research/agent_deep_research/utils.py
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# -*- coding: utf-8 -*-
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"""The utilities for deep research agent"""
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import json
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import os
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import re
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from typing import Any, Sequence, Type, Union
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from agentscope.tool import Toolkit, ToolResponse
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from pydantic import BaseModel
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TOOL_RESULTS_MAX_WORDS = 5000
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def get_prompt_from_file(
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file_path: str,
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return_json: bool,
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) -> Union[str, dict]:
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"""Get prompt from file"""
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with open(os.path.join(file_path), "r", encoding="utf-8") as f:
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if return_json:
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prompt = json.load(f)
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else:
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prompt = f.read()
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return prompt
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def truncate_by_words(sentence: str) -> str:
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"""Truncate too long sentences by words number"""
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words = re.findall(
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r"\w+|[^\w\s]",
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sentence,
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re.UNICODE,
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)
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word_count = 0
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result = []
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for word in words:
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if re.match(r"\w+", word):
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word_count += 1
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if word_count > TOOL_RESULTS_MAX_WORDS:
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break
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result.append(word)
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truncated_sentence = ""
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for i, word in enumerate(result):
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if i == 0:
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truncated_sentence += word
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elif re.match(r"\w+", word):
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truncated_sentence += " " + word
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else:
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truncated_sentence += word
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return truncated_sentence
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def truncate_search_result(
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res: list,
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search_func: str = "tavily-search",
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extract_function: str = "tavily-extract",
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) -> list:
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"""Truncate search result in deep research agent"""
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if search_func != "tavily-search" or extract_function != "tavily-extract":
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raise NotImplementedError(
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"Specific implementation of truncation should be provided.",
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)
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for i, val in enumerate(res):
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res[i]["text"] = truncate_by_words(val["text"])
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return res
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def generate_structure_output(**kwargs: Any) -> ToolResponse:
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"""Generate a structured output tool response.
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This function is designed to be used as a tool function for generating
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structured outputs. It takes arbitrary keyword arguments and wraps them
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in a ToolResponse with metadata.
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Args:
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**kwargs: Arbitrary keyword arguments that should match the format
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of the expected structured output specification.
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Returns:
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ToolResponse: A tool response object with empty content and the
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provided kwargs as metadata.
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Note:
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The input parameters should be in the same format as the specification
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and include as much detail as requested by the calling context.
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"""
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return ToolResponse(content=[], metadata=kwargs)
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def get_dynamic_tool_call_json(data_model_type: Type[BaseModel]) -> list[dict]:
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"""Generate JSON schema for dynamic tool calling with a given data model.
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Creates a temporary toolkit, registers the structure output function,
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and configures it with the specified data model to generate appropriate
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JSON schemas for tool calling.
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Args:
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data_model_type: A Pydantic BaseModel class that defines the expected
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structure of the tool output.
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Returns:
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A list of dictionary that contains the JSON schemas for
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the configured tool, suitable for use in API calls that
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support structured outputs.
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Example:
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class MyModel(BaseModel):
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name: str
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value: int
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schema = get_dynamic_tool_call_json(MyModel)
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"""
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tmp_toolkit = Toolkit()
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tmp_toolkit.register_tool_function(generate_structure_output)
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tmp_toolkit.set_extended_model(
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"generate_structure_output",
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data_model_type,
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)
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return tmp_toolkit.get_json_schemas()
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def get_structure_output(blocks: list | Sequence) -> dict:
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"""Extract structured output from a sequence of blocks.
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Processes a list or sequence of blocks to extract tool use outputs
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and combine them into a single dictionary. This is typically used
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to parse responses from language models that include tool calls.
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Args:
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blocks: A list or sequence of blocks that may contain tool use
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information. Each block should be a dictionary with 'type'
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and 'input' keys for tool use blocks.
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Returns:
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A dictionary containing the combined input data from all tool
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use blocks found in the input sequence.
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Example:
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blocks = [
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{"type": "tool_use", "input": {"name": "test"}},
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{"type": "text", "content": "Some text"},
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{"type": "tool_use", "input": {"value": 42}}
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]
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result = PromptBase.get_structure_output(blocks)
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# result: {"name": "test", "value": 42}
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"""
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dict_output = {}
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for block in blocks:
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if isinstance(block, dict) and block.get("type") == "tool_use":
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dict_output.update(block.get("input", {}))
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return dict_output
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def load_prompt_dict() -> dict:
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"""Load prompt into dict"""
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prompt_dict = {}
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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prompt_dict["add_note"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_worker_additional_sys_prompt.md",
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),
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return_json=False,
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)
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prompt_dict["tool_use_rule"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_tool_usage_rules.md",
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),
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return_json=False,
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)
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prompt_dict["decompose_sys_prompt"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_decompose_subtask.md",
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),
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return_json=False,
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)
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prompt_dict["expansion_sys_prompt"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_deeper_expansion.md",
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),
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return_json=False,
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)
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prompt_dict["summarize_sys_prompt"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_inprocess_report.md",
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),
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return_json=False,
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)
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prompt_dict["reporting_sys_prompt"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_deepresearch_summary_report.md",
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),
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return_json=False,
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)
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prompt_dict["reflect_sys_prompt"] = get_prompt_from_file(
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file_path=os.path.join(
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cur_dir,
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"built_in_prompt/prompt_reflect_failure.md",
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),
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return_json=False,
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)
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prompt_dict["reasoning_prompt"] = (
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"## Current Subtask:\n{objective}\n"
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"## Working Plan:\n{meta_planner_agent}\n"
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"{knowledge_gap}\n"
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"## Research Depth:\n{depth}"
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)
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prompt_dict["previous_plan_inst"] = (
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"## Previous Plan:\n{previous_plan}\n"
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"## Current Subtask:\n{objective}\n"
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)
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prompt_dict["max_depth_hint"] = (
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"The search depth has reached the maximum limit. So the "
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"current subtask can not be further decomposed and "
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"expanded anymore. I need to find another way to get it "
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"done no matter what."
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)
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prompt_dict["expansion_inst"] = (
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"Review the web search results and identify whether "
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"there is any information that can potentially help address "
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"checklist items or fulfill knowledge gaps of the task, "
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"but whose content is limited or only briefly mentioned.\n"
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"**Task Description:**\n{objective}\n"
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"**Checklist:**\n{checklist}\n"
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"**Knowledge Gaps:**\n{knowledge_gaps}\n"
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"**Search Results:**\n{search_results}\n"
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"**Output:**\n"
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)
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prompt_dict["follow_up_judge_sys_prompt"] = (
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"To provide sufficient external information for the user's "
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"query, you have conducted a web search to obtain additional "
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"data. However, you found that some of the information, while "
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"important, was insufficient. Consequently, you extracted the "
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"entire content from one of the URLs to gather more "
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"comprehensive information. Now, you must rigorously and "
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"carefully assess whether, after both the web search and "
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"extraction process, the information content is adequate to "
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"address the given task. Be aware that any arbitrary decisions "
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"may result in unnecessary and unacceptable time costs.\n"
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)
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prompt_dict[
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"retry_hint"
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] = "Something went wrong when {state}. I need to retry."
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prompt_dict["need_deeper_hint"] = (
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"The information is insufficient and I need to make deeper "
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"research to fill the knowledge gap."
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)
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prompt_dict[
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"sufficient_hint"
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] = "The information after web search and extraction is sufficient enough!"
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prompt_dict["no_result_hint"] = (
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"I mistakenly called the `summarize_intermediate_results` tool as "
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"there exists no milestone result to summarize now."
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)
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prompt_dict["summarize_hint"] = (
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"Based on your work history above, examine which step in the "
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"following working meta_planner_agent has been completed. Mark the completed "
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"step with [DONE] at the end of its line (e.g., k. step k [DONE]) "
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"and leave the uncompleted steps unchanged. You MUST return only "
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"the updated meta_planner_agent, preserving exactly the same format as the "
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"original meta_planner_agent. Do not include any explanations, reasoning, "
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"or section headers such as '## Working Plan:', just output the"
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"updated meta_planner_agent itself."
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"\n\n## Working Plan:\n{meta_planner_agent}"
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)
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prompt_dict["summarize_inst"] = (
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"**Task Description:**\n{objective}\n"
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"**Checklist:**\n{knowledge_gaps}\n"
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"**Knowledge Gaps:**\n{working_plan}\n"
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"**Search Results:**\n{tool_result}"
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)
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prompt_dict["update_report_hint"] = (
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"Due to the overwhelming quantity of information, I have replaced the "
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"original bulk search results from the research phase with the "
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"following report that consolidates and summarizes the essential "
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"findings:\n {intermediate_report}\n\n"
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"Such report has been saved to the {report_path}. "
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"I will now **proceed to the next item** in the working meta_planner_agent."
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)
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prompt_dict["save_report_hint"] = (
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"The milestone results of the current item in working meta_planner_agent "
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"are summarized into the following report:\n{intermediate_report}"
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)
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prompt_dict["reflect_instruction"] = (
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"## Work History:\n{conversation_history}\n"
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"## Working Plan:\n{meta_planner_agent}\n"
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)
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prompt_dict["subtask_complete_hint"] = (
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"Subtask ‘{cur_obj}’ is completed. Now the current subtask "
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"fallbacks to '{next_obj}'"
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)
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return prompt_dict
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